Saving weights and logs of step 8

flax_model.msgpack

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0f3199820b0540117aa3b5d3da86bc37b0c35b6a6e4193df2b33f957960feda6
+oid sha256:7b4812d4f42c82ee8a963f90fe45ce25a75b0283d71e093ff578ba3c65de9d6e
 size 498796983

run.sh

@@ -16,4 +16,4 @@
 		--logging_steps="10" \
 		--save_steps="8" \
 		--eval_steps="15" \
-    --push_to_hub
+		--push_to_hub

run_mlm_flax.py

@@ -1,663 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Team All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
-text file or a dataset.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=masked-lm
-"""
-import logging
-import os
-import sys
-import time
-from dataclasses import dataclass, field
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
-import flax
-import jax
-import jax.numpy as jnp
-import optax
-from flax import jax_utils, traverse_util
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from transformers import (
-    CONFIG_MAPPING,
-    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
-    FlaxAutoModelForMaskedLM,
-    HfArgumentParser,
-    PreTrainedTokenizerBase,
-    TensorType,
-    TrainingArguments,
-    is_tensorboard_available,
-    set_seed,
-)
-
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
-    """
-
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The model checkpoint for weights initialization."
-            "Don't set if you want to train a model from scratch."
-        },
-    )
-    model_type: Optional[str] = field(
-        default=None,
-        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    dtype: Optional[str] = field(
-        default="float32",
-        metadata={
-            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
-        },
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    train_ref_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
-    )
-    validation_ref_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
-    max_seq_length: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated. Default to the max input length of the model."
-        },
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    mlm_probability: float = field(
-        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
-    )
-    pad_to_max_length: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
-        },
-    )
-    line_by_line: bool = field(
-        default=False,
-        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
-    )
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForLanguageModeling:
-    """
-    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
-    are not all of the same length.
-
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
-            The probability with which to (randomly) mask tokens in the input.
-
-    .. note::
-
-        For best performance, this data collator should be used with a dataset having items that are dictionaries or
-        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
-        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
-        argument :obj:`return_special_tokens_mask=True`.
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    mlm_probability: float = 0.15
-
-    def __post_init__(self):
-        if self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
-                "You should pass `mlm=False` to train on causal language modeling instead."
-            )
-
-    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
-        # Handle dict or lists with proper padding and conversion to tensor.
-        batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
-
-        # If special token mask has been preprocessed, pop it from the dict.
-        special_tokens_mask = batch.pop("special_tokens_mask", None)
-
-        batch["input_ids"], batch["labels"] = self.mask_tokens(
-            batch["input_ids"], special_tokens_mask=special_tokens_mask
-        )
-        return batch
-
-    def mask_tokens(
-        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
-    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
-        """
-        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
-        """
-        labels = inputs.copy()
-        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
-        probability_matrix = np.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = special_tokens_mask.astype("bool")
-
-        probability_matrix[special_tokens_mask] = 0.0
-        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
-        labels[~masked_indices] = -100  # We only compute loss on masked tokens
-
-        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
-        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
-        # 10% of the time, we replace masked input tokens with random word
-        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
-        indices_random &= masked_indices & ~indices_replaced
-
-        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
-        inputs[indices_random] = random_words[indices_random]
-
-        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-        return inputs, labels
-
-
-def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
-    num_samples = len(samples_idx)
-    samples_to_remove = num_samples % batch_size
-
-    if samples_to_remove != 0:
-        samples_idx = samples_idx[:-samples_to_remove]
-    sections_split = num_samples // batch_size
-    batch_idx = np.split(samples_idx, sections_split)
-    return batch_idx
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
-    summary_writer.scalar("train_time", train_time, step)
-
-    train_metrics = get_metrics(train_metrics)
-    for key, vals in train_metrics.items():
-        tag = f"train_{key}"
-        for i, val in enumerate(vals):
-            summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-if __name__ == "__main__":
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        level="NOTSET",
-        datefmt="[%X]",
-    )
-
-    # Log on each process the small summary:
-    logger = logging.getLogger(__name__)
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
-
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-                cache_dir=model_args.cache_dir,
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-                cache_dir=model_args.cache_dir,
-            )
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
-        if extension == "txt":
-            extension = "text"
-        datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    if model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
-    elif model_args.model_name_or_path:
-        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
-    else:
-        config = CONFIG_MAPPING[model_args.model_type]()
-        logger.warning("You are instantiating a new config instance from scratch.")
-
-    if model_args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    elif model_args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    else:
-        raise ValueError(
-            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
-            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
-        )
-
-    # Preprocessing the datasets.
-    # First we tokenize all the texts.
-    if training_args.do_train:
-        column_names = datasets["train"].column_names
-    else:
-        column_names = datasets["validation"].column_names
-    text_column_name = "text" if "text" in column_names else column_names[0]
-
-    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
-    if data_args.line_by_line:
-        # When using line_by_line, we just tokenize each nonempty line.
-        padding = "max_length" if data_args.pad_to_max_length else False
-
-        def tokenize_function(examples):
-            # Remove empty lines
-            examples = [line for line in examples if len(line) > 0 and not line.isspace()]
-            return tokenizer(
-                examples,
-                return_special_tokens_mask=True,
-                padding=padding,
-                truncation=True,
-                max_length=max_seq_length,
-            )
-
-        tokenized_datasets = datasets.map(
-            tokenize_function,
-            input_columns=[text_column_name],
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    else:
-        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
-        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
-        # efficient when it receives the `special_tokens_mask`.
-        def tokenize_function(examples):
-            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
-
-        tokenized_datasets = datasets.map(
-            tokenize_function,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
-        # max_seq_length.
-        def group_texts(examples):
-            # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
-            total_length = len(concatenated_examples[list(examples.keys())[0]])
-            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
-            # customize this part to your needs.
-            total_length = (total_length // max_seq_length) * max_seq_length
-            # Split by chunks of max_len.
-            result = {
-                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
-                for k, t in concatenated_examples.items()
-            }
-            return result
-
-        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
-        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
-        # might be slower to preprocess.
-        #
-        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
-        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
-        tokenized_datasets = tokenized_datasets.map(
-            group_texts,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    # Enable tensorboard only on the master node
-    has_tensorboard = is_tensorboard_available()
-    if has_tensorboard and jax.process_index() == 0:
-        try:
-            from flax.metrics.tensorboard import SummaryWriter
-
-            summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-        except ImportError as ie:
-            has_tensorboard = False
-            logger.warning(
-                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
-            )
-    else:
-        logger.warning(
-            "Unable to display metrics through TensorBoard because the package is not installed: "
-            "Please run pip install tensorboard to enable."
-        )
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
-    model = FlaxAutoModelForMaskedLM.from_config(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
-
-    # Store some constant
-    num_epochs = int(training_args.num_train_epochs)
-    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
-    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-
-    num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
-
-    # Create learning rate schedule
-    warmup_fn = optax.linear_schedule(
-        init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
-    )
-    decay_fn = optax.linear_schedule(
-        init_value=training_args.learning_rate,
-        end_value=0,
-        transition_steps=num_train_steps - training_args.warmup_steps,
-    )
-    linear_decay_lr_schedule_fn = optax.join_schedules(
-        schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
-    )
-
-    # We use Optax's "masking" functionality to not apply weight decay
-    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
-    # mask boolean with the same structure as the parameters.
-    # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
-    def decay_mask_fn(params):
-        flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
-        return traverse_util.unflatten_dict(flat_mask)
-
-    # create adam optimizer
-    adamw = optax.adamw(
-        learning_rate=linear_decay_lr_schedule_fn,
-        b1=training_args.adam_beta1,
-        b2=training_args.adam_beta2,
-        eps=1e-8,
-        weight_decay=training_args.weight_decay,
-        mask=decay_mask_fn,
-    )
-
-    # Setup train state
-    state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)
-
-    # Define gradient update step fn
-    def train_step(state, batch, dropout_rng):
-        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
-        def loss_fn(params):
-            labels = batch.pop("labels")
-
-            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
-            # compute loss, ignore padded input tokens
-            label_mask = jnp.where(labels > 0, 1.0, 0.0)
-            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-            # take average
-            loss = loss.sum() / label_mask.sum()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(loss_fn)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = jax.lax.pmean(
-            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
-        )
-
-        return new_state, metrics, new_dropout_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Define eval fn
-    def eval_step(params, batch):
-        labels = batch.pop("labels")
-
-        logits = model(**batch, params=params, train=False)[0]
-
-        # compute loss, ignore padded input tokens
-        label_mask = jnp.where(labels > 0, 1.0, 0.0)
-        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-        # compute accuracy
-        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
-
-        # summarize metrics
-        metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
-        metrics = jax.lax.psum(metrics, axis_name="batch")
-
-        return metrics
-
-    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-
-    train_time = 0
-    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
-    for epoch in epochs:
-        # ======================== Training ================================
-        train_start = time.time()
-        train_metrics = []
-
-        # Create sampling rng
-        rng, input_rng = jax.random.split(rng)
-
-        # Generate an epoch by shuffling sampling indices from the train dataset
-        num_train_samples = len(tokenized_datasets["train"])
-        train_samples_idx = jax.random.permutation(input_rng, jnp.arange(num_train_samples))
-        train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
-
-        # Gather the indexes for creating the batch and do a training step
-        for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
-            samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
-            model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
-            # Model forward
-            model_inputs = shard(model_inputs.data)
-            state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
-            train_metrics.append(train_metric)
-
-            cur_step = epoch * (num_train_samples // train_batch_size) + step
-
-            if cur_step % training_args.logging_steps == 0 and cur_step > 0:
-                # Save metrics
-                train_metric = jax_utils.unreplicate(train_metric)
-                train_time += time.time() - train_start
-                if has_tensorboard and jax.process_index() == 0:
-                    write_train_metric(summary_writer, train_metrics, train_time, cur_step)
-
-                epochs.write(
-                    f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
-                )
-
-                train_metrics = []
-
-            if cur_step % training_args.eval_steps == 0 and step > 0:
-                # ======================== Evaluating ==============================
-                num_eval_samples = len(tokenized_datasets["validation"])
-                eval_samples_idx = jnp.arange(num_eval_samples)
-                eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
-                eval_metrics = []
-                for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
-                    samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
-                    model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
-                    # Model forward
-                    model_inputs = shard(model_inputs.data)
-                    metrics = p_eval_step(state.params, model_inputs)
-                    eval_metrics.append(metrics)
-
-                # normalize eval metrics
-                eval_metrics = get_metrics(eval_metrics)
-                eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
-                eval_normalizer = eval_metrics.pop("normalizer")
-                eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
-                # Update progress bar
-                epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
-
-                # Save metrics
-                if has_tensorboard and jax.process_index() == 0:
-                    cur_step = epoch * (len(tokenized_datasets["train"]) // train_batch_size)
-                    write_eval_metric(summary_writer, eval_metrics, cur_step)
-
-            if cur_step % training_args.save_steps == 0 and step > 0:
-                # save checkpoint after each epoch and push checkpoint to the hub
-                if jax.process_index() == 0:
-                    params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
-                    model.save_pretrained(
-                        training_args.output_dir,
-                        params=params,
-                        push_to_hub=training_args.push_to_hub,
-                        commit_message=f"Saving weights and logs of step {cur_step}",
-                    )

run_mlm_flax.py

@@ -0,0 +1 @@
+/home/patrick/transformers/examples/flax/language-modeling/run_mlm_flax.py